The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.

In this column, we introduce our Model AI Assignment, A Module on Ethical Thinking about Autonomous Vehicles in an AI Course, and more broadly introduce a conversation on ethics education in AI education.

Data science and machine learning should not only be research areas for scientists and researchers but should also be accessible and understandable to the general audience. Enabling students to understand the details behind the technology will support them in becoming aware consumers and encourage them to become active participants. In this paper, we present instructional materials developed for introducing students to two key machine learning algorithms: decision trees and k-nearest neighbors. The materials were tested in a middle school's afterschool artificial intelligence program with four participating students aged 12 to 13. A combination of hands-on activities, innovative technology, and intuitive examples facilitated student learning. With hand-drawn decision trees and penguin species classifications, students used the algorithms to solve problems and anticipate other possible applications. We present the technology used, curriculum materials developed, and classroom structure. Following the guidelines from AI4K12 and introducing foundational machine learning algorithms, we hope to foster student interest in STEM fields.

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy.